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Predicting Dengue Fever Incidence and Disease Dynamics under Climate Change in Southeast Asia
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| 3.61 MB | Adobe PDF |
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Resumo(s)
Dengue fever is a climate-sensitive vector-borne disease primarily transmitted by Aedes
mosquitoes, A. aegypti and A. albopictus. Previous research has analyzed the relationship
between climate and disease, with varying outcomes. Temperature and precipitation have
been demonstrated as relevant predictors in most studies. The effects of climate change on
dengue fever were found to be uncertain, highlighting the need for further study.
This study analyzed how environmental variables interact with disease transmission, enabling
predictive modeling to forecast dengue incidence. For deployment, climate change
simulations were used as a framework to assess the disease’s response to changing
environmental factors. The incidence and environmental data for 17 Southeast Asian locations
were collected from the national Ministries of Health and the National Oceanic and
Atmospheric Administration (NOAA) from 2016-2023. Traditional machine learning and deep
learning models were used to forecast dengue incidence based on ten input features of
temperature, precipitation, and lagged observations.
The predictive ability was evaluated using Mean Absolute Error (MAE) and Root Mean Squared
Error (RMSE). Deep and machine learning models showed similar results for predicting dengue
incidence. The Convolutional Neural Network (CNN) achieved the lowest error with an average
MAE of 10.10 and RMSE of 13.61 on the validation set. Models showed varying predictive
abilities across locations. Despite extensive data preparation, some locations performed
worse on all models, indicating potential issues with initial data quality. Errors were reduced
for all models on the test set, with CNN demonstrating superior with an average MAE of 5.06
and RMSE of 7.09. Although errors decreased with additional data, model performance could
benefit from additional variables that were not included.
Lastly, CNN was deployed to assess the disease’s response to climate change. The predicted
model was found to be sensitive to simulated changes in total precipitation and mean
temperature. Results show positive and negative changes in the annual incidence rates for
both emission scenarios, with a positive linear trend observed for mean temperature.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Dengue Fever Incidence Forecast Deep Learning Machine Learning Climate Change SDG 3 - Good health and well-being